Accessing the semantic and lexical information of constituents while typing compounds
Bibliographic record
Abstract
Abstract A key question in research concerning the typing production of morphologically complex words is whether the whole multimorphemic word is output ballistically or whether individual constituents are accessed during typing. To address this question, we examined keystroke latencies during the production of English compounds (e.g., snowball ) to test whether the initiation and continued typing of each constituent (e.g., snow and ball ) are influenced by its linguistic properties (length, frequency, and semantic transparency). Participants identified and then typed a compound word. We found that the initiation and continued typing of each constituent was influenced by the linguistic properties of that constituent. However, the linguistic properties of the second constituent also influenced the typing latency of the final letter of the first constituent, suggesting that production of the first constituent overlapped with accessing and planning the keystrokes of the second constituent. The influence of the linguistic properties of the first constituent on its own initiation and continued typing suggests that accessing and planning the keystrokes of the first constituent occurred as the compound word was being identified. Our findings indicate that individual constituents are accessed during production and influence the typing of compound words.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".